Update app.py

app.py

@@ -2,6 +2,7 @@ import spaces
 import gradio as gr
 import torch
 from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, AutoencoderKL
+from scheduling_tcd import TCDScheduler
 from PIL import Image
 import os
 import time
@@ -41,14 +42,18 @@ def load_model(lora_model):
     pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
         "cagliostrolab/animagine-xl-3.1", controlnet=controlnet, vae=vae, torch_dtype=dtype
     )
+    pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+    
     pipe.enable_model_cpu_offload()
 
     # LoRAモデルの設定
     if lora_model == "とりにく風":
-        pipe.load_lora_weights(lora_dir, weight_name="tori29umai_line.safetensors", adapter_name="tori29umai_line")
-        pipe.set_adapters(["tori29umai_line"], adapter_weights=[0.7])
+        pipe.load_lora_weights(lora_dir, weight_name="tcd-animaginexl-3_1.safetensors", adapter_name="tcd-animaginexl-3_1")
+        pipe.load_lora_weights(lora_dir, weight_name="tori29umai_line.safetensors", adapter_name="tori29umai_line")        
+        pipe.set_adapters(["tcd-animaginexl-3_1", "tori29umai_line"], adapter_weights=[1.0, 1.0])
     elif lora_model == "プレーン":
-        pass  # プレーンの場合はLoRAを読み込まない
+        pipe.load_lora_weights(lora_dir, weight_name="tcd-animaginexl-3_1.safetensors", adapter_name="tcd-animaginexl-3_1")    
+        pipe.set_adapters(["tcd-animaginexl-3_1"], adapter_weights=[1.0])
 
     # 現在のLoRAモデルを保存
     current_lora_model = lora_model
@@ -85,8 +90,9 @@ def predict(lora_model, input_image_path, prompt, negative_prompt, controlnet_sc
         negative_prompt=negative_prompt,
         controlnet_conditioning_scale=float(controlnet_scale),
         generator=generator,
-        num_inference_steps=30,
-        eta=1.0,
+        num_inference_steps=4,
+        guidance_scale=0,
+        eta=0.3, 
     ).images[0]
     print(f"Time taken: {time.time() - last_time}")
     output_image = output_image.resize(input_image.size, Image.LANCZOS)